| Language of instruction : English |
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Sequentiality
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Mandatory sequentiality bound on the level of programme components
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Following programme components must have been included in your study programme in a previous education period
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Concepts of Probability and Statistics (1798)
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5.0 stptn |
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There is no data for this choice.
Change the language, year or choose another item in the dropdown list if it is available.
| Degree programme | | Study hours | Credits | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 2nd year Master Bioinformatics | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical |  |
| 2nd year Master Bioinformatics - icp | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 2nd year Master Biostatistics | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 2nd year Master Biostatistics - icp | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 2nd year Master Data Science | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 2nd year Master Quantitative Epidemiology | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 2nd year Master Quantitative Epidemiology - icp | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- EC
| The student is capable of acquiring new knowledge. | - EC
| The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application. | | | - DC
| The student is able to correctly use the theory methodologically, thus contributing to scientific research within the field of statistical and data science.
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The student needs to understand and to be able to apply/calculate the basic concepts in probability theory and statistics: random variable, continous/discrete distributions, expectation, variance, multivariate/marginal distribution, independence of random variables, conditional probability/density, central limit theorem, multivariate normal distribution.
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This course deals with the theoretical basis of parametric statistical inference. Key concepts of estimation, confidence regions and hypothesis testing are introduced and applied to several parametric statistical models.
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Lecture ✔
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Response lecture ✔
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Period 1 Credits 3,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 15 % |
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| Transfer of partial marks within the academic year | ✔ |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Previously purchased compulsory textbooks |
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[Mathematical Statistics and Data Analysis],[John Rice],[],[Brooks/Cole],[9780495118688],[] |
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| Recommended reading |
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- [Statistical Inference],[G. Casella; R.L. Berger],[second],[Brooks/Cole Cengage Learning],[9780534243128],[]
- [An introduction to mathematical statistics],[Bijma, Jonker and Van Der Vaart],[],[Amsterdam Univeristy Press],[],[Available as e-book: https://ebookcentral-proquest-com.bib-proxy.uhasselt.be/lib/ubhasselt/detail.action?docID=5046611&pq-origsite=summon]
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| Exchange Programme Statistics | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- EC
| The student is capable of acquiring new knowledge. | - EC
| The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application. | | | - DC
| The student is able to correctly use the theory methodologically, thus contributing to scientific research within the field of statistical and data science.
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The student needs to understand and to be able to apply/calculate the basic concepts in probability theory and statistics: random variable, continous/discrete distributions, expectation, variance, multivariate/marginal distribution, independence of random variables, conditional probability/density, central limit theorem, multivariate normal distribution.
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This course deals with the theoretical basis of parametric statistical inference. Key concepts of estimation, confidence regions and hypothesis testing are introduced and applied to several parametric statistical models.
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Lecture ✔
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Response lecture ✔
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Period 1 Credits 3,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 15 % |
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| Transfer of partial marks within the academic year | ✔ |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Previously purchased compulsory textbooks |
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[Mathematical Statistics and Data Analysis],[John Rice],[],[Brooks/Cole],[9780495118688],[] |
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| Recommended reading |
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- [Statistical Inference],[G. Casella; R.L. Berger],[second],[Brooks/Cole Cengage Learning],[9780534243128],[]
- [An introduction to mathematical statistics],[Bijma, Jonker and Van Der Vaart],[],[Amsterdam Univeristy Press],[],[Available as e-book: https://ebookcentral-proquest-com.bib-proxy.uhasselt.be/lib/ubhasselt/detail.action?docID=5046611&pq-origsite=summon]
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| Master of Teaching in Sciences and Technology - Engineering and Technology choice for subject didactics math | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- EC
| WET 1. The newly graduated student has advanced knowledge, insight, skills and attitudes in the disciplines relevant to his/her specific subject didactics and is able to communicate these appropriately to his/her stakeholders. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The student needs to understand and to be able to apply/calculate the basic concepts in probability theory and statistics: random variable, continous/discrete distributions, expectation, variance, multivariate/marginal distribution, independence of random variables, conditional probability/density, central limit theorem, multivariate normal distribution.
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This course deals with the theoretical basis of parametric statistical inference. Key concepts of estimation, confidence regions and hypothesis testing are introduced and applied to several parametric statistical models.
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Lecture ✔
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Response lecture ✔
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Period 1 Credits 3,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 15 % |
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| Transfer of partial marks within the academic year | ✔ |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Previously purchased compulsory textbooks |
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[Mathematical Statistics and Data Analysis],[John Rice],[],[Brooks/Cole],[9780495118688],[] |
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|
| Recommended reading |
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- [Statistical Inference],[G. Casella; R.L. Berger],[second],[Brooks/Cole Cengage Learning],[9780534243128],[]
- [An introduction to mathematical statistics],[Bijma, Jonker and Van Der Vaart],[],[Amsterdam Univeristy Press],[],[Available as e-book: https://ebookcentral-proquest-com.bib-proxy.uhasselt.be/lib/ubhasselt/detail.action?docID=5046611&pq-origsite=summon]
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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.16.9, section 2. |
3 Education, Examination and Legal Position Regulations art.15.1, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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